The paper "On the Relationship between Similarity Measures and Thresholds of Statistical Significance in the Context of Comparing Fuzzy Sets" (by Josie McCulloch, Zack Ellerby and Christian Wagner) has been accepted for publication and is available now here: doi.org/10.1109/TFUZZ.2019.2922161

Abstract:
Comparing fuzzy sets by computing their similarity is common, with a large set of measures of similarity available. However, while commonplace in the computational intelligence community, the application and results of similarity measures are less common in the wider scientific context, where statistical approaches are the standard for comparing distributions. This is challenging, as it means that developments around similarity measures arising from the fuzzy community are inaccessible to the wider scientific community; and that the fuzzy community fails to take advantage of a strong statistical understanding which may be applicable to comparing (fuzzy membership) functions. In this paper, we commence a body of work on systematically relating the outputs of similarity measures to the notion of statistically significant difference; that is, how (dis)similar do two fuzzy sets need to be for them to be statistically different? We explain that in this context it is useful to initially focus on dis-similarity, rather than similarity, as the former aligns directly with the widely used concept of statistical difference. We propose two methods of applying statistical tests to the outputs of fuzzy dissimilarity measures to determine significant difference. We show how the proposed work provides deeper insight into the behaviour and possible interpretation of degrees of dis-similarity and, consequently, similarity, and how the interpretation differs in respect to context (e.g., the complexity of the fuzzy sets).

The paper "Similarity between interval-valued fuzzy sets taking into account the width of the intervals and admissible orders" (by H. Bustince, C. Marco-Detchart, J. Fernandez, C. Wagner, J.M. Garibaldi, Z. Takác) has been accepted for publication to Fuzzy Sets and Systems: https://doi.org/10.1016/j.fss.2019.04.002
For abstract and highlights, see:  christianwagner.weebly.com/

Earlier this year, I was invited to contribute to the panel on 'AI Governance: Role of the legislators, tech companies and standard bodies' at CPDP 2019 in Brussels, Belgium. Big thanks to Mark Cole, Andra Giurgiu and the University of Luxembourg for organising and hosting an exciting and timely panel (and for inviting me, even though I know nothing about governance :) ). Also, thank you to the CPDP organisers - it was a great, really stimulating and extremely well organised conference! 

A video of the panel is now available here: https://www.youtube.com/watch?v=3ZJg-2D2QIA, with brief details on what to expect below.
​All the best,
Christian

Panel organised by Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg Chair Mark Cole, Co-organiser Andra Giurgiu, University of Luxembourg (LU), 

Moderator: Erik Valgaeren, Stibbe (BE)
Speakers: Alain Herrmann, National Commission for Data Protection (LU); Christian Wagner, University of Nottingham (UK); Jan Schallaböck, iRights/ISO (DE); Janna Lingenfelder, IBM/ ISO (DE)

AI calls for a “coordinated action plan” as recently stated by the European Commission. With its societal and ethical implications, it is a matter of general impact across sectors, going beyond security and trustworthiness or the creation of a regulatory framework. Hence this panel intends to address the topic of AI governance, whether such governance is needed and if so, how to ensure its consistency. It will also discuss whether existing structures and bodies are adequate to deal with such governance, or, if we perhaps need to think about creating new structures and man- date them with this task. Where do we stand and where are we heading in terms of how we are collectively dealing with the soon to be almost ubiquitous phenomenon of AI?

• Do we need AI governance? If so, who should be in charge of it?
• Is there a need to ensure consistency of such governance?
• What are the risks? Do we know them and are we in the right position to address them?
• Are existing structures/bodies sufficient to address these issues or do we perhaps need to
create news ones?

LUCID members meet for Christmas dinner.

LUCID members meet in Nottingham city centre to celebrate our member Elissa Madi following her viva passing. Throughout her PhD research she focused on Type-2 Fuzzy TOPSIS and worked upon improving Multi-Criteria Decision Making Models. We all in LUCID wish Elissa the very best in the next stage of her life!

Congratulations to LUCID member Elissa Madi who passed her viva on Friday subject to minor corrections!
Elissa was primarily supervised by Prof Jon Garibaldi.  Her thesis is entitled ‘An Improved Uncertainty in Multi-Criteria Decision Making Model Based on Type-2 Fuzzy TOPSIS’.

Cyber Security Threat Data Analyst - KTP Associate (fixed term)
Closing Date: Friday, 5th October 2018
Based primarily at J.P. Morgan, Canary Wharf, London

This is an exciting opportunity for an ambitious individual to advance their career through a Knowledge Transfer Partnership (KTP). You will be working with JP Morgan and the School of Computer Science at the University of Nottingham to develop and embed a novel methodology to deliver improved forward assessment of the likelihood of cyber security threats in respect to a variety of uncertain data.

You will be employed by the University of Nottingham (School of Computer Science) but will be based primarily at JP Morgan, Canary Wharf, London.

This post will be offered on a fixed-term contract for a period of 36 months.

See here for more details on the role and to apply.

SyFSeL is a free open-source library that automatically generates synthetic fuzzy sets. It is aimed for use in empirically testing methods developed for fuzzy sets. SyFSeL generates as many sets as desired, with specified membership function type (normal, bi-modal or multi-modal) and fuzzy set type (type-1 or type-2) to enable users to emulate real data. Fuzzy sets are stored in csv format so users can easily import the generated sets into their own fuzzy systems software and SyFSeL can also create graphical plots of the generated sets.

The library is available through the software page of the LUCID website and is available here.

For more information on the library and how to use it, see the related paper here.

LUCID is proud to announce that its PGR Student member Shaily Kabir has been awarded with the 2018 IEEE Computational Intelligence Society Graduate Research Grant. This grant is given to deserving PhD students with meritorious projects who seek to carry out their research, therefore, Shaily will work during this coming summer break period with Dr. Timothy C. Havens at the Michigan Technological University (USA).

Congratulations Shaily!

Based on a collaboration with NTU Singapore, a new paper on leveraging the more faithful tracking of input uncertainty in the context of Quadcopter Unmanned Aerial Vehicle (UAV) control has been accepted for publication in the IEEE/ASME Transactions on Mechatronics.
​An early access copy is available via the DOI here: 
​"Input Uncertainty Sensitivity Enhanced Non-Singleton Fuzzy Logic Controllers for Long-Term Navigation of Quadrotor UAVs":  https://doi.org/10.1109/TMECH.2018.2810947